sFDvent: A global trait database for deep‐sea hydrothermal‐vent fauna
Type | Article | ||||||||||||||||||||||||||||
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Date | 2019-11 | ||||||||||||||||||||||||||||
Language | English | ||||||||||||||||||||||||||||
Author(s) | Chapman Abbie S. A.1, 2, Beaulieu Stace E.3, Colaço Ana4, 5, Gebruk Andrey V.6, Hilario Ana7, Kihara Terue C.8, Ramirez‐llodra Eva9, Sarrazin Jozee![]() |
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Affiliation(s) | 1 : Ocean and Earth Science University of Southampton Waterfront Campus, National Oceanography Centre Southampton ,United Kingdom 2 : Centre for Biodiversity and Environment Research University College London London,United Kingdom 3 : Woods Hole Oceanographic Institution Massachusetts, USA 4 : IMAR Instituto do Mar, Departamento de Oceanografia e Pescas University of the Azores Horta, Portugal 5 : MARE ‐ Marine Environmental Sciences Centre Horta, Portugal 6 : Shirshov Institute of Oceanology, Russian Academy of Sciences Moscow,Russia 7 : CESAM & Biology Department University of Aveiro Aveiro ,Portugal 8 : Senckenberg am Meer, German Center for Marine Biodiversity Research (DZMB) Wilhelmshaven ,Germany 9 : Norwegian Institute for Water Research Oslo, Norway 10 : Ifremer, Centre de Bretagne REM/EEP, Laboratoire Environnement Profond Plouzané ,France 11 : Department of Biology and School of Earth & Ocean Sciences University of Victoria British Columbia ,Canada 12 : Life Sciences Department Natural History Museum London ,United Kingdom 13 : Seascape Consultants Ltd Ampfield, Romsey Hampshire ,United Kingdom 14 : X‐STAR, Japan Agency for Marine‐Earth Science and Technology (JAMSTEC) Kanagawa, Japan 15 : Muséum national d'Histoire naturelle, Institut Systématique Evolution Biodiversité (ISYEB) CNRS, Sorbonne Université, EPHE Paris ,France 16 : Temple University Philadelphia Pennsylvania, USa 17 : Muséum national d'Histoire naturelle Mécanismes de Communication et Adaptation des Micro‐organismes MNHN CNRS UMR7245Paris ,France 18 : Department of Biology Pennsylvania State University State College Pennsylvania, USA 19 : Fisheries and Oceans Canada, Institute of Ocean Sciences Sidney British Columbia ,Canada 20 : NIOZ Royal Netherlands Institute for Sea Research, Department of Ocean Systems Utrecht University Den Burg (Texel) ,The Netherlands 21 : National Oceanography Centre, University of Southampton Waterfront Campus Southampton ,United Kingdom 22 : Station Biologique de Roscoff, UMR 7144, CNRS ‐ Sorbonne Université, Place G. Teissier Roscoff, France 23 : British Antarctic Survey, High Cross Cambridge ,United Kingdom 24 : Academy of Scientific and Innovative Research (AcSIR), Biological Oceanography Division CSIR‐National Institute of Oceanography Goa ,India 25 : Department of Biology and KG Jebsen Centre for Deep‐Sea Research University of Bergen Bergen, Norway 26 : Marine Sciences ‐ School of Natural and Environmental Sciences Newcastle University Newcastle upon Tyne, United Kingdom 27 : Independent Researcher Southampton ,United Kingdom 28 : Department of Zoology Swedish Museum of Natural History Stockholm ,Sweden 29 : Division of Marine Science and Conservation Nicholas School of the Environment, Duke University Beaufort North Carolina, USA 30 : School of Biological Sciences and Swire Institute of Marine Science The University of Hong Kong, Kadoorie Biological Sciences Building Hong Kong SAR, China 31 : Memorial University of Newfoundland St. John’s Newfoundland and Labrador, Canada |
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Source | Global Ecology And Biogeography (1466-822X) (Wiley), 2019-11 , Vol. 28 , N. 11 , P. 1538-1551 | ||||||||||||||||||||||||||||
DOI | 10.1111/geb.12975 | ||||||||||||||||||||||||||||
WOS© Times Cited | 33 | ||||||||||||||||||||||||||||
Keyword(s) | biodiversity, collaboration, conservation, cross-ecosystem, database, deep sea, functional trait, global-scale, hydrothermal vent, sFDvent | ||||||||||||||||||||||||||||
Abstract | Motivation Traits are increasingly being used to quantify global biodiversity patterns, with trait databases growing in size and number, across diverse taxa. Despite growing interest in a trait-based approach to the biodiversity of the deep sea, where the impacts of human activities (including seabed mining) accelerate, there is no single repository for species traits for deep-sea chemosynthesis-based ecosystems, including hydrothermal vents. Using an international, collaborative approach, we have compiled the first global-scale trait database for deep-sea hydrothermal-vent fauna - sFDvent (sDiv-funded trait database for the Functional Diversity of vents). We formed a funded working group to select traits appropriate to: (a) capture the performance of vent species and their influence on ecosystem processes, and (b) compare trait-based diversity in different ecosystems. Forty contributors, representing expertise across most known hydrothermal-vent systems and taxa, scored species traits using online collaborative tools and shared workspaces. Here, we characterise the sFDvent database, describe our approach, and evaluate its scope. Finally, we compare the sFDvent database to similar databases from shallow-marine and terrestrial ecosystems to highlight how the sFDvent database can inform cross-ecosystem comparisons. We also make the sFDvent database publicly available online by assigning a persistent, unique DOI. Main types of variable contained Six hundred and forty-six vent species names, associated location information (33 regions), and scores for 13 traits (in categories: community structure, generalist/specialist, geographic distribution, habitat use, life history, mobility, species associations, symbiont, and trophic structure). Contributor IDs, certainty scores, and references are also provided. Spatial location and grain Global coverage (grain size: ocean basin), spanning eight ocean basins, including vents on 12 mid-ocean ridges and 6 back-arc spreading centres. Time period and grain sFDvent includes information on deep-sea vent species, and associated taxonomic updates, since they were first discovered in 1977. Time is not recorded. The database will be updated every 5 years. Major taxa and level of measurement Deep-sea hydrothermal-vent fauna with species-level identification present or in progress. Software format .csv and MS Excel (.xlsx). | ||||||||||||||||||||||||||||
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